Image Analysis Method Research Articles

Natural fibre-enhanced CO2 transport and uptake in cement pastes subjected to enforced carbonation

Mineral carbonation is an effective CO2 sequestration approach and can be applied to cement-based materials through enforced carbonation curing. To accelerate this process, this study investigated the use of natural fibres (hemp, jute, cotton, and wool) to enhance CO2 transport in cement composites. Spun fibre yarns were added to cement pastes and subjected to enforced carbonation (20 vol% CO2) at 25 °C and 1 atm for 24–168 hours. Carbonation profiles were evaluated using two methods: phenolphthalein indicator coupled with image analysis, and thermogravimetric analysis (TGA). A novel image analysis method, based on colour deconvolution of phenolphthalein-stained images, was developed. Results from this method were consistent with those from TGA, offering a reliable and rapid technique for quantitatively assessing carbonation profiles of cement-based materials. The results confirmed that the natural fibre yarns created preferential CO2 transport pathways to facilitate CO2 uptake. Hemp yarns led to the most significant acceleration of CO2 transport, with carbonation fronts reaching 50 mm depths within 24 hours. Samples without fibres only showed superficial carbonation at the exposure surface. The findings of this study indicate the feasibility of using natural fibres to improve CO₂ sequestration in cement-based materials.

Prognosis of asymptomatic endolymphatic hydrops in healthy volunteers: A five-year cohort study.

This study aimed to clarify the prognosis of asymptomatic endolymphatic hydrops (EH) in healthy volunteers via five-year follow-ups with inner ear magnetic resonance imaging (MRI). Inner ear MRI was performed on 115 participants recruited as controls in a previous study on Meniere's disease. The endolymphatic space was visualized using Naganawa's method of contrast-enhanced MRI with intravenous gadolinium injection and evaluated using Nakashima's method of 2D imaging analysis. Cochlear or vestibular EH was present in 7.0% of participants (n = 8), with all cases being unilateral (laterality), moderate (severity), and asymptomatic (onset). Only cochlear-localized EH, only vestibular-localized EH, and both EH were present in 1.7% (n = 2) (C group), 4.3% (n = 5) (V group), and 0.9% (n = 1) (CV group) of participants, respectively. Conducting inner ear MRI after 5 years showed that EH had almost disappeared in two participants in the C and V groups (4/8, 50.0%). EH was still present in three participants in the V group and one in the CV group (4/8, 50.0%). One participant in the V group and another in the CV group presented with residual inner ear EH and developed typical symptomatic Meniere's disease (2/8, 25.0%). Approximately 7% of healthy participants showed asymptomatic EH. Therefore, EH is not the definitive marker for making a diagnosis of Meniere's disease or the suitable predictor for the development of Meniere's disease. Among these participants, 25% maintained EH and subsequently developed typical Meniere's disease within the next 5 years. Schellong-positive participants maintained persistent EH in the inner ear, and participants with higher scores on the self-rating depression scale developed Meniere's symptoms after 5 years. 2a.

Relationship between void characteristics and re-liquefaction resistance: An image analysis study

To scrutinize the impact of void characteristics on re-liquefaction resistance, a series of constant-volume cyclic bi-axial tests was conducted on an assembly of plastic rods. The first and second liquefaction stages involved the application of isotropic compression at 100 kPa followed by constant-volume cyclic loading with the deviator stress set at 30 or 60 kPa. This study introduced an innovative image analysis method to quantify four void characteristics: anisotropy index (Ie) and average void element size (Ae) for the element-based analysis, and local anisotropy index (Ie,ij) and local void ratio (eij) for the grid-based analysis. The newly developed anisotropy index was seen to facilitate the assessment of the primary alignment and degree of anisotropy in void elements. The results confirmed that an increase in re-liquefaction resistance is evident in specimens with lower average eij, coefficient of variation (CV) of eij, and Ie, indicating denser, more homogeneous, and isotropic conditions. Nevertheless, specimens with a greater degree of anisotropy were found to be more susceptible to re-liquefaction. The development of strain in the early stages of cyclic loading was found to be predominantly influenced by the anisotropy index, underscoring the imperative need for an enhanced method that can predict liquefaction resistance, as well as re-liquefaction resistance, and incorporates the anisotropy index.

A mineralogical and geochemical investigation of archaeological pottery shards found at Bukit Komel, Sungai Tembeling, Pahang

This study presents the results of mineralogical and geochemical analyses carried out on prehistoric pottery of Bukit Komel in Sungai Tembeling, Pahang. The Bukit Komel archaeological site, which was excavated in 2022, has produced a significant number of earthenware potteries mainly in the form of body and rim shards. Since information on the prehistory pottery of Sungai Tembeling is limited, it was decided that morphological and analytical techniques should be performed in order to gain insights on pottery physical characteristics, technology and source. The morphological analysis technique used on Bukit Komel pottery involves the study of pottery shape, size, colour and design while the scientific analysis comprises X-ray diffraction (XRD), X-ray fluorescence (XRF), thin section petrography and image analysis methods. Based on geochemical and mineralogical results, this study concludes that the Bukit Komel pottery was locally made by using clay sourced from Sungai Tembeling. This was also confirmed by the findings of comparative study conducted between the Bukit Komel pottery and clay extracted from Sungai Tembeling. Similar minerals found amidst the pottery and clay are quartz, albite, muscovite and microcline. Technology-wise, the Bukit Komel pottery was hand-made using the paddle and anvil technique, and sand-tempered. Pottery shapes include flat- and round-bottomed vessels with plain, red-slipped, impressed and incised designs. Based on colour analysis, the Bukit Komel pottery was very likely fired using the open-firing technique at low temperatures, perhaps between 400 °C and 600 °C.

Fiber distribution in UHPC under different influencing factors evaluated with a novel method based on deep learning

The dispersion of fibers in UHPC significantly affects its mechanical properties. Currently, conventional image analysis methods are employed to evaluate fiber dispersion. However, due to the settling of steel fibers in UHPC and the subjectivity of image processing, the evaluation of fiber dispersion is neither sufficiently thorough nor accurate. In this study, removal method was used to optimize a deep learning-based U-net model for fiber identification. And sinkage coefficient was proposed as a complementary index for evaluating fiber dispersion. Subsequently, the accuracy of both the identification technique and evaluation method was separately validated. Then, the image processing techniques and evaluation method proposed in this study are applied to investigate the impact of various factors on fiber distribution in UHPC. The results indicate that the optimized deep learning-based network accomplished batch image semantic segmentation and image denoising. This approach mitigated the subjectivity during threshold selection and minimized the influence of low-quality images on fiber identification. The proposed sinkage coefficient can reflect the impact of fiber sinking on its performance. This coefficient associate with distribution and orientation coefficients can evaluate fiber dispersion comprehensively. Experimental investigations revealed that during vibration, fibers in UHPC exhibit sinking or floating phenomena. The consistency of the slurry emerges as a pivotal factor affecting fiber dispersion. Lower consistency results in pronounced fiber sinking, whereas higher consistency induce fiber floating. The incorporation of coarse aggregate facilitates the formation of a network structure with steel fibers, thereby ameliorating fiber sinking or floating tendencies, further affecting the fiber dispersion. Additionally, the increase in vibration time is also detrimental to the uniform dispersion of fibers.

QTL mapping of almond kernel quality traits in the F1 progeny of ‘Marcona’ × ‘Marinada’

Almond breeding is increasingly focusing on kernel quality. However, unlike other agronomic traits, the genetic basis of physical and chemical kernel quality traits has been poorly investigated. To address this gap, we conducted a QTL mapping of these traits to enhance our understanding of their genetic control. We phenotyped fruit samples from an F1 population derived from the cross between ‘Marcona’ and ‘Marinada’ for up to four years, using conventional and image analysis methods. Additionally, the 91 individuals of the population were genotyped with the almond Axiom™ 60K SNP array, and high-density linkage maps were constructed. These analyses identified several genomic regions of breeding interest. For example, two regions on chromosome one were found to contain QTLs for kernel shape and dimension, while another region at the end of the same chromosome contained QTLs for kernel fatty acid composition. Notably, QTLs for kernel symmetry and kernel shoulder, reported for the first time in this study, were also mapped on chromosome one. These QTLs will serve as a foundation for developing molecular markers linked to kernel physical and chemical quality traits in almonds, facilitating the integration of marker-assisted selection into breeding programs.

AI powered detection and assessment of onychomycosis: A spotlight on yellow and deep learning

AI powered detection and assessment of onychomycosis: A spotlight on yellow and deep learning

Head Tumor Segmentation and Detection Based on Resunet

Deep learning algorithms are designed to mimic the functions of the human brain. These algorithms can simulate the human brain for feature extraction, i.e. neural networks with many hidden layers. This algorithm is being trained and tested using the MRI brain tumor segmentation dataset provided by Kaggle. ResNet50 is used for tumor classification tasks due to its powerful feature extraction capability. Through its deep residual structure, ResNet50 can effectively extract different lesion features and improve classification accuracy. At the same time, ResUNet combines the feature extraction capabilities of ResNet with the segmentation advantages of U-Net, accurately capturing tumor boundaries and achieving high-quality tumor segmentation. For the MRI dataset, the accurate segmentation rate of the model is 91.68%, which provides a certain reference for the medical field. In summary, this study demonstrates the usability of combining ResUNet50 and ResNet in brain tumor detection. In the field of medical image analysis, accurate brain tumor segmentation and classification are crucial for clinical diagnosis and treatment planning, and provide new ideas and methods for future medical image analysis and clinical applications.

Analysis of Microscopic Remaining Oil Based on the Fluorescence Image and Deep Learning.

Fossil fuels like oil and natural gas continue to be the primary sources of global energy. Enhancing hydrocarbon recovery from exploited reservoirs has been a major scientific concern in the petroleum industry. Following extended exploitation, the reservoir's oil-water dynamics become intricate, thereby complicating petroleum and natural gas extraction. Pore-scale analysis of microscopic remaining oil (micro-remaining oil) offers theoretical underpinning for enhancing production from high-water-cut oil reservoirs. Fluorescence thin-section analysis allows for the direct evaluation of reservoir oil-bearing properties using oil-containing samples, providing insights into the occurrence and distribution patterns of micro-remaining oil without requiring time-consuming core displacement experiments. The high resolution of fluorescence images further establishes this technique as a representative method for studying micro-remaining oil. However, conventional fluorescence image analysis methods are often subjective and labor-intensive. To address this limitation, we trained four deep learning networks-U-Net, ResU-Net, ScSEU-Net, and Unet++-and applied them innovatively to automate fluorescence image segmentation. Evaluation of network performance via statistical metrics and visual observation indicated that all four networks achieved high segmentation accuracy, particularly ResU-Net, which showed robustness against over-segmentation, under-segmentation, and image noise. Finally, leveraging optimal segmentation results, we conducted quantitative analyses of oil saturation, micro-remaining oil patterns, and pore occupancy. The study demonstrated that ternary composite agents substantially decreased the presence of cluster, film, and adsorbed oils byreducing the oil-water mobility ratio and lowering oil-water interfacial tension. Primarily, these agents displaced crude oil from pores larger than 60 micrometers in an equivalent radius, leading to a significant reduction in their content. Nevertheless, substantial quantities of micro-remaining oil are still confined in pores smaller than 50 micrometers in an equivalent radius, emphasizing the need for attention during subsequent development adjustments. Our research has notably improved the efficiency and accuracy of fluorescence image analysis, effectively supporting the enhancement of recovery in high-water-cut oil reservoirs.

Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin

Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

Proposal of a Rapid Detection System Using Image Analysis for ELISA with an Autonomous Centrifugal Microfluidic System.

In this study, with the aim of adapting an enzyme-linked immunosorbent assay (ELISA) system for point-of-care testing (POCT), we propose an image analysis method for ELISAs using a centrifugal microfluidic device that automatically executes the assay. The developed image analysis method can be used to quantify the color development reaction on a TMB (3,3',5,5'-tetramethylbenzidine) substrate. In a conventional ELISA, reaction stopping reagents are required at the end of the TMB reaction. In contrast, the developed image analysis method can analyze color in the color-developing reaction without a reaction stopping reagent. This contributes to a reduction in total assay time. The microfluidic devices used in this study could execute reagent control for ELISAs by steady rotation. In the demonstration of the assay and image analysis, a calibration curve for mouse IgG detection was successfully prepared, and it was confirmed that the image analysis method had the same performance as the conventional analysis method. Moreover, the changes in the amount of color over time confirmed that a calibration curve equal to the endpoint analysis was obtained within 2 min from the start of the TMB reaction. As the assay time before the TMB reaction was approximately 7.5 min, the developed ELISA system could detect TMB in just 10 min. In conventional methods using a plate reader, the assay required a time of 90 min for manual handling using microwell plates, and in the case of using automatic microfluidic devices, 30 min were required. The time of 10 min realized by this proposed method is equal to the time required for detection in an immunochromatographic assay with a lateral flow assay; therefore, it is expected that ELISAs can be performed sufficiently to adapt to POCT.

Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis

This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production.

Application of Binary Image Quality Assessment Methods to Predict the Quality of Optical Character Recognition Results

One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results’ quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results.

Improving CNN Fish Detection and Classification with Tracking

The regular and consistent monitoring of marine ecosystems and fish communities is becoming more and more crucial due to increasing human pressures. To this end, underwater camera technology has become a major tool to collect an important amount of marine data. As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. However, such algorithms are rather image-based and do not exploit the potential of video data. In this paper, we propose a method of coupling video tracking and CNN image analysis to perform a robust and accurate fish classification on deep sea videos and improve automatic classification accuracy. Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.

Training for Emergencies - How Germany is Preparing for Large-Scale Emergencies Using the EUROMED 2024 Civil Protection Exercise as an Example

Abstract. Every year, numerous major events with thousands of participants take place around the world, for which extensive safety measures are taken to ensure the well-being and safety of visitors. Nevertheless, incidents that lead to injuries and deaths occur time and again. This year, Germany is looking forward to the UEFA EURO 2024, an international football championship for which the host country, Germany, has made extensive preparations on several levels. One component of this is the Medical Task Force (MTF) of the Federal Office of Civil Protection and Disaster Assistance (BBK), which has trained a possible medical service deployment in the event of a mass casualty incident with a specially coordinated series of exercises. The extent to which current remote sensing data can make a useful contribution and be meaningfully integrated was analyzed during the large-scale EUROMED exercise with the help of an airborne camera system on board a helicopter. The system provided up-to-date aerial images of the exercise site, but was also used to record the traffic situation during the transfer of the vehicle convoys to the exercise site. In addition, state-of-the-art AI-based image analysis methods were tested on site and the results were provided and evaluated. An important operational exercise to bring science and practice together step by step in order to be better prepared for an emergency.

Effectiveness and impact factors of passive convergence-permeable reactive barrier (PC-PRB): Insights from tracer simulation study

Passive convergence-permeable reactive barrier (PC-PRB) represents a green and sustainable technology for in-situ remediation of contaminated groundwater. A laboratory-scale PC-PRB tracer simulation system was established to quantify its contaminant plume capture performance using image analysis method. Results indicate that PC-PRB captures the plume 65% wider than C-PRB, which means that fewer PRB sizes and materials volume would be necessary to treat an equivalent contaminated plume. This improvement is due to a significant drawdown within the PC-PRB's passive well, known as the passive hydraulic decompression-convergent flow effect. We further evaluated the effects of water pipe length, hydraulic gradient, and media particle size on PC-PRB's plume capture performance. Results indicate that an increased water pipe length enhances the PC-PRB's plume capture capacity due to greater well drawdown. PC-PRB not only captures the plume but also acts as a hydraulic barrier. The retardation effect of PC-PRB on plume migration increases with water pipe length. Conversely, both hydraulic gradient and media particle size impact the plume capture capacity of PC-PRB by modifying groundwater flow velocity and pollutant dispersion. An increase in either hydraulic gradient or media particle size decreases the plume capture performance of PC-PRB. Therefore, PC-PRB technology may be more effective in contaminated sites characterized by low hydraulic gradients and permeability. Overall, PC-PRB demonstrates significant effectiveness in enhancing plume capture performance, which can notably reduce remediation costs and environmental footprint, broadening its application scope.

Filling the gaps: Introducing plasticizers into π-conjugated OPE-NH2 Langmuir layers for defect-free anisotropic interfaces and membranes towards unidirectional mass, charge, or energy transfer

The construction of ultrathin membranes from linearly aligned π-electron systems is advantageous for targeted energy, charge, or mass transfer. The Langmuir-Blodgett (LB) technique enables the creation of such membranes, especially with amphiphilic π-electron systems. However, these systems often aggregate, forming rigid Langmuir monolayers with defects or holes. In this study we introduce plasticizers to effectively address this issue. To create anisotropic membranes, we used an oligo(phenylene ethynylene) derivative (OPE-NH2) as an linear amphiphile and bisphenol A di-tert-butyl ester (BPAE) as a plasticizer. We analyzed surface pressure (mean molecular area) (Π(mma)) isotherms and characterized Langmuir monolayers with Brewster Angle Microscopy (BAM), to determine the optimal miscibility of OPE-NH2 with BPAE. Detailed analysis of hole areas filled was performed through image binarization. We identified an optimal BPAE concentration of 4 mol-% in the OPE-NH2 Langmuir monolayer. Our BAM image evaluation via binarization determined the difference between the mean molecular areas of close-packed Langmuir domains and those quantified via the Π(mma) isotherm. This study presents an automated method for BAM image analysis and a new approach for fabricating defect-free anisotropic molecular monolayers of π-conjugated amphiphiles.

Deep learning-based image analysis with RTFormer network for measuring 2D crystal size distribution during cooling crystallization of β form L-glutamic acid

In this paper, a deep learning-based image analysis method is proposed for in-situ measurement of two-dimensional (2D) crystal size distribution during the cooling crystallization process of β form L-glutamic acid (β-LGA). Firstly, an image quality assessment strategy is presented for in-situ snapshotted crystal images to distinguish different crystallization stages, followed by image enhancement for the snapshotted images in each stage to facilitate analysis. Then, an edge-guided network based on the RTFormer network is developed to acquire precise crystal image segmentation and boundary location, thus improving the identification accuracy on crystal image boundary and its internal body. The network performance is further enhanced by using hyperparameter optimization and a class balance strategy. Subsequently, another identification strategy is developed to distinguish agglomerated and overlapped crystal images, so as to acquire more individual crystals for statistical measurement. Finally, the 2D size of each crystal is calculated based on the major axis and maximum inscribed circle of its segmented image. Experiments on measuring the 2D size distributions of crystal populations during β-LGA crystallization process are performed to verify the accuracy and efficiency of the proposed measurement method.

Cement vs aggregates and textures of aggregates in a mortar: Comparative image analysis methods and analytical protocols

The types, abundance and texture of phases are crucial for reconstructing the manufacturing of historical and contemporary construction materials such as mortars. Commonly, these data are obtained on polished mesoscopic and/or thin section surfaces and imaged with several techniques. Here, a thin section from an already well-characterised mortar was analysed to unveil the amount (area%) of cement paste vs aggregates, plus the textural features of the aggregates. The thin section was imaged by a high-resolution scanner (HRS), by transmission optical microscopy (TOM) and scanning electron microscopy (SEM). The single HRS image discriminates only quartz (qz) from cement+af+ss (af: alkali-feldspar, ss: sheetsilicates). The stitched TOM image distinguishes cement from aggregates, i.e. qz+af+ss, whereas the stitched SEM image discriminates cement, pores, qz and af+ss. The amount of cement vs aggregates and the area of the different aggregates determined by SEM is more accurate since it reflects chemical attributes. 2D Fuller curves were constructed considering different types of 2D dimensional parameters extracted from both TOM and SEM digital images. Intermediate dimensional parameters for both TOM and SEM had the best match with the 3D sieving Fuller curve. SEM shows the most detailed and adaptable recognition of cement to aggregate ratio and quantification of aggregate clasts, but it is time-consuming and expensive. HRS is rapid but only crudely accurate. TOM is more accurate and less expedite than HRS; by contrast, TOM is faster than SEM but unable to distinguish different aggregate clasts with similar optical features.

Measuring pore sizes of nonwoven geotextiles by X-ray CT images: a comparative evaluation

This paper focuses on evaluating the pore sizes of four nonwoven geotextile specimens with different pore size distribution techniques. The paper introduces a bubble analysis technique to find the two-dimensional (2D) pore size distribution of geotextile specimens. A new method is also introduced to determine the three-dimensional (3D) constriction sizes of nonwoven geotextiles from X-ray CT images. The digital image concept and image processing methods for the 2D image analysis and 3D constriction size measurement of geotextile specimens are explained. Finally, the paper compares the 3D constriction sizes with those measured from 2D image analysis, mercury intrusion porosimetry, manufacturer-reported opening size values based on the wet sieving method, and pore sizes calculated from theoretical equations. The results show that the largest pore opening sizes measured from the 3D constriction size method are in good agreement with the theoretical equations. The values measured from the wet sieving method were found to be the smallest, indicating the limitations of the wet sieving method to determine the largest pore opening size of nonwoven geotextiles. As it is the constriction size which must control filtration behaviour, the 3D constriction size method is recommended for assessing the reference porometry.